{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ssms` package serves two purposes. \n",
    "\n",
    "1. Easy access to *fast simulators of sequential sampling models*\n",
    "   \n",
    "2. Support infrastructure to construct training data for various approaches to likelihood / posterior amortization\n",
    "\n",
    "We provide two minimal examples here to illustrate how to use each of the two capabilities.\n",
    "\n",
    "Let's start with *installing* the `ssms` package.\n",
    "\n",
    "You can do so by typing,\n",
    "\n",
    "`pip install git+https://github.com/AlexanderFengler/ssm_simulators`\n",
    "\n",
    "in your terminal.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ssms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the Simulators\n",
    "\n",
    "Let's start with using the basic simulators. \n",
    "You access the main simulators through the  `ssms.basic_simulators.simulator` function.\n",
    "\n",
    "To get an idea about the models included in `ssms`, use the `config` module.\n",
    "The central dictionary with metadata about included models sits in `ssms.config.model_config`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ddm',\n",
       " 'ddm_legacy',\n",
       " 'angle',\n",
       " 'weibull',\n",
       " 'levy',\n",
       " 'levy_angle',\n",
       " 'full_ddm',\n",
       " 'gamma_drift',\n",
       " 'gamma_drift_angle',\n",
       " 'ds_conflict_drift',\n",
       " 'ds_conflict_drift_angle',\n",
       " 'ornstein']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check included models\n",
    "list(ssms.config.model_config.keys())[:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'ds_conflict_drift',\n",
       " 'params': ['a',\n",
       "  'z',\n",
       "  't',\n",
       "  'tinit',\n",
       "  'dinit',\n",
       "  'tslope',\n",
       "  'dslope',\n",
       "  'tfixedp',\n",
       "  'tcoh',\n",
       "  'dcoh'],\n",
       " 'param_bounds': [[0.3, 0.1, 0.001, 0, 0, 0.01, 0.01, 0, -1.0, -1.0],\n",
       "  [3.0, 0.9, 2.0, 5.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0]],\n",
       " 'boundary': <function ssms.basic_simulators.boundary_functions.constant(t=0)>,\n",
       " 'drift_fun': <function ssms.basic_simulators.drift_functions.ds_conflict_drift(t=array([0.000e+00, 1.000e-03, 2.000e-03, ..., 9.997e+00, 9.998e+00,\n",
       "       9.999e+00]), init_p_t=0, init_p_d=0, slope_t=1, slope_d=1, fixed_p_t=1, coherence_t=1.5, coherence_d=1.5)>,\n",
       " 'n_params': 10,\n",
       " 'default_params': [2.0, 0.5, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 0.5, -0.5],\n",
       " 'hddm_include': ['a',\n",
       "  'z',\n",
       "  't',\n",
       "  'tinit',\n",
       "  'dinit',\n",
       "  'tslope',\n",
       "  'dslope',\n",
       "  'tfixedp',\n",
       "  'tcoh',\n",
       "  'dcoh'],\n",
       " 'nchoices': 2}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take an example config for a given model\n",
    "ssms.config.model_config[\"ds_conflict_drift\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**\n",
    "The usual structure of these models includes,\n",
    "\n",
    "- Parameter names (`'params'`)\n",
    "- Bounds on the parameters (`'param_bounds'`)\n",
    "- A function that defines a boundary for the respective model (`'boundary'`)\n",
    "- The number of parameters (`'n_params'`)\n",
    "- Defaults for the parameters (`'default_params'`)\n",
    "- The number of choices the process can produce (`'nchoices'`)\n",
    "\n",
    "The `'hddm_include'` key concerns information useful for integration with the [hddm](https://github.com/hddm-devs/hddm) python package, which facilitates hierarchical bayesian inference for sequential sampling models. It is not important for the present tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "theta = ssms.config.model_config[\"ds_conflict_drift\"][\"default_params\"]\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"tfixedp\")] = 5\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"a\")] = 1.5\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"tinit\")] = 5\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"dinit\")] = 0\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"tcoh\")] = 1\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"dcoh\")] = 0\n",
    "theta[ssms.config.model_config[\"ds_conflict_drift\"][\"params\"].index(\"t\")] = 0\n",
    "sim_out = simulator(model=\"ds_conflict_drift\", theta=theta, n_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "theta = ssms.config.model_config[\"ddm\"][\"default_params\"]\n",
    "theta[ssms.config.model_config[\"ddm\"][\"params\"].index(\"v\")] = 5\n",
    "theta[ssms.config.model_config[\"ddm\"][\"params\"].index(\"a\")] = 1.5\n",
    "theta[ssms.config.model_config[\"ddm\"][\"params\"].index(\"t\")] = 0\n",
    "sim_out_base = simulator(model=\"ddm\", theta=theta, n_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'z',\n",
       " 't',\n",
       " 'tinit',\n",
       " 'dinit',\n",
       " 'tslope',\n",
       " 'dslope',\n",
       " 'tfixedp',\n",
       " 'tcoh',\n",
       " 'dcoh']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssms.config.model_config[\"ds_conflict_drift\"][\"params\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13aab21f0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vrvLrwEPAP/cdGAH+ttv3PcBU37beT++i1m76QngJtR1K74zsZX3LVny86H0Vfwz4Mb35x/PGOT7AFHBv95y/ofvl9SLr2k1vHnXmGNvW9f2d7v3dBdwB/PZC+x/0GhdZ19jet+6Yvb17rV8GXrzYurrlnwXOn9V3JcdrUDZM7Bjzp/+S1IjVNocuSRrAQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmN+D/taoxlNBQrvQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# import seaborn as sns\n",
    "plt.plot(sim_out[\"metadata\"][\"drift\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.0, 3.0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAQI0lEQVR4nO3df2xdZ33H8feXliahQFyIVQW7kStRsVVkG8jqQK4QStlWCCKdxCoYYhnrFCHBUsIm8EBas01IRpsIME1oEWULUsuoWqZWS7fR1UUsldrNLR1pGxhR57qx0sYZ2MAIsI7v/rgnndv8cHLPuT7Xj98vybr3nHvOPd8jRx8/ee7zPDcyE0lSuV7UdgGSpN4y6CWpcAa9JBXOoJekwhn0klS4C9suAGDDhg05MjLSdhmStKI89NBDxzNzcKnj+iLoR0ZGmJqaarsMSVpRIuLJcznOrhtJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS/1wp7NnR+pD/TFEghScRZm2q5Aeo4tekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0UsPGJibbLkF6HoNeatjs/Im2S5Cex6CXemhsYtIWvlrnhCmph2zdqx/Yopekwi0Z9BHxhYg4FhGPLtr3ioi4JyK+Uz1eUu2PiPhsRByOiG9GxOt7WbwkaWnn0qL/G+DaF+wbB+7NzCuAe6ttgLcCV1Q/O4DPNVOmJKlbSwZ9Zn4d+O4Ldm8D9lXP9wHXLdr/xex4ABiIiI0N1SpJ6kK3ffSXZubR6vnTwKXV8yHgqUXHHan2nSIidkTEVERMzc3NdVmGJGkptT+MzcwEsovz9mbmaGaODg4O1i1DknQG3Qb9Mye7ZKrHY9X+WeCyRccNV/ukVWtoYB0j4/sdT6/WdBv0dwHbq+fbgTsX7f+tavTNG4CFRV080qp0//gWpie2OqZerVlywlREfAl4M7AhIo4ANwETwG0RcQPwJHB9dfjdwNuAw8CPgPf1oGZJ0nlYMugz891neOma0xybwAfqFiVJao4zYyWpcAa9JBXOoJd6Zf0mDqzZ2XYVkkEv9cyugwzH8barkFymWGrUns1Mr53hSG5guO1apIpBLzVpYYaRH98KwHS7lUjPsetGkgpn0EtS4Qx6SSqcQS/1wMmFzKR+4IexUg/cP76l82R3q2VIgC16SSqeQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJcaNjSwru0SpOcx6KWGPbdypdQnDHpJKpxBL0mFM+glqXAGvSQVrlbQR8SuiHgsIh6NiC9FxNqIuDwiHoyIwxHx5Yi4qKliJUnnr+ugj4ghYCcwmpmvBS4A3gV8EtiTma8Gvgfc0EShkqTu1O26uRBYFxEXAi8BjgJbgNur1/cB19W8hiSphq6DPjNngT8HZugE/ALwEDCfmc9Whx0Bhk53fkTsiIipiJiam5vrtgxJ0hLqdN1cAmwDLgdeBVwMXHuu52fm3swczczRwcHBbsuQJC2hTtfNW4D/zMy5zPwf4CvAGDBQdeUADAOzNWuUJNVQJ+hngDdExEsiIoBrgMeB+4B3VsdsB+6sV6IkqY46ffQP0vnQ9WHgYPVee4GPAh+OiMPAK4GbG6hTWvGGBtYxNjHZdhlahS5c+pAzy8ybgJtesPsJ4Ko67yuV6P7xLYyM72+7DK1CzoyVpMIZ9JJUOINekgpn0EtS4Wp9GCupY2xiktn5E0yvbbsS6VS26KUGzM6fYHpia9tlSKdl0EtN2bMZ1m9quwrpFHbdSE1ZmIHdC21XIZ3CoJeWw57NsDDDgTUbALt4tLzsupGWQ9XaH47jbVeiVcigl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEv9ZoTqdQyx9FLveZEKrXMFr0kFc6gl6TCGfSSVDiDXpIKZ9BLDTiwZqcja9S3HHUjNWA4jsMuR9aoP9mil6TCGfSSVDiDXpIKZ9BLUuH8MFbqJUfiqA8Y9FIv7TrYdgWSXTfSchsZ38/YxGTbZWgVqRX0ETEQEbdHxLci4lBEvDEiXhER90TEd6rHS5oqVirB9MRWZudPtF2GVpG6LfrPAP+YmT8H/CJwCBgH7s3MK4B7q21JUku6DvqIWA+8CbgZIDN/mpnzwDZgX3XYPuC6eiVKkuqo06K/HJgD/joivhERn4+Ii4FLM/NodczTwKWnOzkidkTEVERMzc3N1ShDknQ2dYL+QuD1wOcy83XAf/OCbprMTCBPd3Jm7s3M0cwcHRwcrFGGJOls6gT9EeBIZj5Ybd9OJ/ifiYiNANXjsXolSpLq6DroM/Np4KmIeE216xrgceAuYHu1bztwZ60KJUm11J0w9XvALRFxEfAE8D46fzxui4gbgCeB62teQ5JUQ62gz8xHgNHTvHRNnfeVJDXHmbGSVDiDXpIKZ9BLUuEMemk5rd8Eeza3XYVWGYNeWk67DsLCTNtVaJUx6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINeqmvPZo7khvM+bWxikrGJyR4UJD1f3e+MlbQww9U/uZXp8zxtdv5EL6qRTmGLXmrA0MC6tkuQzsiglxpw//iWtkuQzsigl6TCGfSSVDiDXmrB0MA6+/W1bBx1I7XgZJ/+yPj+livRamCLXpIKZ9BLUuEMekkqnEEvSYWrHfQRcUFEfCMi/r7avjwiHoyIwxHx5Yi4qH6ZkqRuNdGivxE4tGj7k8CezHw18D3ghgauIUnqUq2gj4hhYCvw+Wo7gC3A7dUh+4Dr6lxDklRP3Rb9p4GPAD+rtl8JzGfms9X2EWDodCdGxI6ImIqIqbm5uZplSJLOpOugj4i3A8cy86Fuzs/MvZk5mpmjg4OD3ZYhSVpCnZmxY8A7IuJtwFrg5cBngIGIuLBq1Q8Ds/XLlCR1q+sWfWb+YWYOZ+YI8C5gMjPfA9wHvLM6bDtwZ+0qJUld68U4+o8CH46Iw3T67G/uwTWklWv9Jtizue0qtIo0sqhZZn4N+Fr1/AngqibeVyrSroOwe33bVWgVcWasJBXOoJekwhn0UouGBtYxMr6fsYnJtktRwfziEalFfgGJloMtekkqnEEvSYUz6KU2OJZey8igl9qw6yAszLRdhVYJg16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9FJbnDSlZWLQS21x0pSWiUEvSYUz6CWpcAa91AcOrNlpf716xi8ekfrAcByHhbarUKls0UtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfRSHxkZ38/YxGTbZagwBr3UR6YntjI7f6LtMlSYroM+Ii6LiPsi4vGIeCwibqz2vyIi7omI71SPlzRXrtQ/xiYmGRnfX+9N1m+C3es7j1KP1GnRPwv8fmZeCbwB+EBEXAmMA/dm5hXAvdW2VJzZ+RNMT2yt9ya7DsLuhc6j1CNdB31mHs3Mh6vnPwAOAUPANmBfddg+4LqaNUqSamikjz4iRoDXAQ8Cl2bm0eqlp4FLz3DOjoiYioipubm5JsqQJJ1G7aCPiJcCdwAfyszvL34tMxPI052XmXszczQzRwcHB+uWIUk6g1pBHxEvphPyt2TmV6rdz0TExur1jcCxeiVKkuqoM+omgJuBQ5n5qUUv3QVsr55vB+7svjxJUl111qMfA94LHIyIR6p9HwMmgNsi4gbgSeD6WhVKkmrpOugz8wAQZ3j5mm7fV5LULGfGSnXs2exkJ/U9v0pQqmNhpjPhSepjtuglqXAGvSQVzq4bqV9UC5wdWLMBqLmGjrSIQS/1i2phs+Hd61suRKWx60bqM0dyQ2c0j9QQg17qM1f/5LOd0TxSQwx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDgnTEnd2LOZ6bUzPVm5cmhgHfwYxiYmmZ0/wdDAOu4f39L4dbR62KKXurEww8iPb31uNmuTTob67PwJpie2Mjt/ovFraHWxRS/1sz2bObDmR7j2jeqwRS91aWhgXc/e+0huYHrtbwIwHMcZm5js2bVUPoNe6lIv+82v/slnn9c1ZPeN6rDrRupDvfzfglYfg17qQ46yUZPsupGkwhn0klQ4u26kFcaJVDpftuilFcaJVDpftuillWLPZliY8cvDdd5s0UsrxcIM7F5gOI63XYlWGINeWgEOrNn53AJqfnm4zpdBL60Aw3H8uVmyfnm4zpd99FK/W7+JowsneOP4fqCaNbtmEwdyJ2MTnRm0jr7R2fQk6CPiWuAzwAXA5zNzohfXkVaFXQfZCEw/b+dBhnevd+SNzknjXTcRcQHwl8BbgSuBd0fElU1fR1LHgTU7Yff6U/rtxyYmGRnf78qX6kkf/VXA4cx8IjN/CvwtsK0H15FE1X+/e+GUfnvH2+ukyMxm3zDincC1mfm71fZ7gV/OzA++4LgdwI5q87XAo40W0l82ACWPiSv5/kq+N/D+VrrXZObLljqotQ9jM3MvsBcgIqYyc7StWnrN+1u5Sr438P5WuoiYOpfjetF1Mwtctmh7uNonSWpBL4L+34ArIuLyiLgIeBdwVw+uI0k6B4133WTmsxHxQeCf6Ayv/EJmPrbEaXubrqPPeH8rV8n3Bt7fSndO99f4h7GSpP7iEgiSVDiDXpIK1zdBHxF/GhHfjIhHIuKrEfGqtmtqUkT8WUR8q7rHv4uIgbZrakpE/EZEPBYRP4uIYoayRcS1EfHtiDgcEeNt19OkiPhCRByLiCLnr0TEZRFxX0Q8Xv3bvLHtmpoSEWsj4l8j4t+re/vjJc/plz76iHh5Zn6/er4TuDIz399yWY2JiF8FJqsPqz8JkJkfbbmsRkTEzwM/A/4K+IPMPKexvf2sWsrjP4BfAY7QGU327sx8vNXCGhIRbwJ+CHwxM1/bdj1Ni4iNwMbMfDgiXgY8BFxXwu8vIgK4ODN/GBEvBg4AN2bmA2c6p29a9CdDvnIx0B9/gRqSmV/NzGerzQfozC8oQmYeysxvt11Hw4peyiMzvw58t+06eiUzj2bmw9XzHwCHgKF2q2pGdvyw2nxx9XPWvOyboAeIiE9ExFPAe4A/arueHvod4B/aLkJnNQQ8tWj7CIUExWoTESPA64AHWy6lMRFxQUQ8AhwD7snMs97bsgZ9RPxzRDx6mp9tAJn58cy8DLgF+ODZ363/LHV/1TEfB56lc48rxrncm9RvIuKlwB3Ah17Qa7CiZeb/ZuYv0ekZuCoiztr9tqxr3WTmW87x0FuAu4GbelhO45a6v4j4beDtwDXZLx+OnKPz+N2VwqU8Vriq//oO4JbM/Erb9fRCZs5HxH3AtZxlYci+6bqJiCsWbW4DvtVWLb1QfRnLR4B3ZOaP2q5HS3IpjxWs+sDyZuBQZn6q7XqaFBGDJ0ftRcQ6OgMGzpqX/TTq5g7gNXRGbzwJvD8zi2lBRcRhYA3wX9WuB0oZVRQRvw78BTAIzAOPZOavtVpUAyLibcCn+f+lPD7RbkXNiYgvAW+ms4zvM8BNmXlzq0U1KCKuBv4FOEgnUwA+lpl3t1dVMyLiF4B9dP5dvgi4LTP/5Kzn9EvQS5J6o2+6biRJvWHQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpML9H2RFJAFvZxTfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sim_out[\"rts\"] * sim_out[\"choices\"], bins=30, histtype=\"step\")\n",
    "plt.hist(sim_out_base[\"rts\"] * sim_out_base[\"choices\"], bins=30, histtype=\"step\")\n",
    "\n",
    "plt.xlim((-3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the simulator is a `dictionary` with three elements.\n",
    "\n",
    "1. `rts` (array)\n",
    "2. `choices` (array)\n",
    "3. `metadata` (dictionary)\n",
    "\n",
    "The `metadata` includes the named parameters, simulator settings, and more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST STANDARD TRAINING DATA GENERATOR\n",
    "# my_data_config = config.data_generator_config['lan']['cnn']\n",
    "my_data_config = ssms.config.data_generator_config[\"lan\"][\"mlp\"]\n",
    "my_data_config[\"dgp_list\"] = \"tradeoff_no_bias\"\n",
    "my_data_config[\"n_parameter_sets\"] = 100\n",
    "my_data_config[\"n_samples\"] = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model_config = ssms.config.model_config[\"tradeoff_no_bias\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cpus used:  6\n",
      "checking:  data/lan_mlp/\n"
     ]
    }
   ],
   "source": [
    "my_dataset_generator = ssms.dataset_generators.data_generator(\n",
    "    generator_config=my_data_config, model_config=my_model_config\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation round: 1  of 10\n",
      "simulation round: 2  of 10\n",
      "simulation round: 3  of 10\n",
      "simulation round: 4  of 10\n",
      "simulation round: 5  of 10\n",
      "simulation round: 6  of 10\n",
      "simulation round: 7  of 10\n",
      "simulation round: 8  of 10\n",
      "simulation round: 9  of 10\n",
      "simulation round: 10  of 10\n"
     ]
    }
   ],
   "source": [
    "x = my_dataset_generator.generate_data_training_uniform(save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[-0.38610438, -1.3320698 ,  1.8300703 , ...,  0.407555  ,\n",
       "          2.104763  ,  0.        ],\n",
       "        [-0.38610438, -1.3320698 ,  1.8300703 , ...,  0.407555  ,\n",
       "          2.69727   ,  0.        ],\n",
       "        [-0.38610438, -1.3320698 ,  1.8300703 , ...,  0.407555  ,\n",
       "          1.2684623 ,  0.        ],\n",
       "        ...,\n",
       "        [-0.39633387,  0.18759471, -1.0542347 , ...,  0.20308132,\n",
       "         -0.32188985,  0.        ],\n",
       "        [-0.39633387,  0.18759471, -1.0542347 , ...,  0.20308132,\n",
       "         -0.06727885,  3.        ],\n",
       "        [-0.39633387,  0.18759471, -1.0542347 , ...,  0.20308132,\n",
       "         -0.74756646,  3.        ]], dtype=float32),\n",
       " 'labels': array([ -1.5323812 ,  -2.2718892 ,  -0.79332405, ..., -66.77497   ,\n",
       "        -66.77497   , -66.77497   ], dtype=float32),\n",
       " 'generator_config': {'output_folder': 'data/lan_mlp/',\n",
       "  'dgp_list': 'tradeoff_no_bias',\n",
       "  'nbins': 0,\n",
       "  'n_samples': 1000,\n",
       "  'n_parameter_sets': 100,\n",
       "  'n_parameter_sets_rejected': 100,\n",
       "  'n_training_samples_by_parameter_set': 1000,\n",
       "  'max_t': 20.0,\n",
       "  'delta_t': 0.001,\n",
       "  'pickleprotocol': 4,\n",
       "  'n_cpus': 6,\n",
       "  'kde_data_mixture_probabilities': [0.8, 0.1, 0.1],\n",
       "  'simulation_filters': {'mode': 20,\n",
       "   'choice_cnt': 0,\n",
       "   'mean_rt': 17,\n",
       "   'std': 0,\n",
       "   'mode_cnt_rel': 0.9},\n",
       "  'negative_rt_cutoff': -66.77497,\n",
       "  'n_subruns': 10,\n",
       "  'bin_pointwise': False,\n",
       "  'separate_response_channels': False},\n",
       " 'model_config': {'name': 'tradeoff_no_bias',\n",
       "  'params': ['vh', 'vl1', 'vl2', 'a', 'd', 't'],\n",
       "  'param_bounds': [[-4.0, -4.0, -4.0, 0.3, 0.0, 0.0],\n",
       "   [4.0, 4.0, 4.0, 2.5, 1.0, 2.0]],\n",
       "  'boundary': <function ssms.basic_simulators.boundary_functions.constant(t=0)>,\n",
       "  'n_params': 6,\n",
       "  'default_params': [0.0, 0.0, 0.0, 1.0, 0.5, 1.0],\n",
       "  'hddm_include': ['vh', 'vl1', 'vl2', 'a', 'd', 't'],\n",
       "  'nchoices': 4}}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TEST NESTED MODEL TRAINING DATA\n",
    "# my_data_config = config.data_generator_config['lan']['cnn']\n",
    "my_data_config = ssms.config.data_generator_config[\"lan\"][\"mlp\"]\n",
    "my_data_config[\"dgp_list\"] = \"glob\"\n",
    "my_data_config[\"n_parameter_sets\"] = 100\n",
    "my_data_config[\"n_samples\"] = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model_config = ssms.config.model_config[\"glob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_cpus:  6\n",
      "checking:  data/lan_mlp/\n"
     ]
    }
   ],
   "source": [
    "my_dataset_generator = ssms.dataset_generators.data_generator(\n",
    "    generator_config=my_data_config, model_config=my_model_config\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation round:  1  of 10\n",
      "simulation round:  2  of 10\n",
      "simulation round:  3  of 10\n",
      "simulation round:  4  of 10\n",
      "simulation round:  5  of 10\n",
      "simulation round:  6  of 10\n",
      "simulation round:  7  of 10\n",
      "simulation round:  8  of 10\n",
      "simulation round:  9  of 10\n",
      "simulation round:  10  of 10\n"
     ]
    }
   ],
   "source": [
    "my_output = my_dataset_generator.generate_data_nested(save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[ 1.0340211 ,  1.        ],\n",
       "        [ 1.0720211 ,  1.        ],\n",
       "        [ 1.3030207 ,  1.        ],\n",
       "        ...,\n",
       "        [ 0.7438603 , -1.        ],\n",
       "        [ 0.48186138,  1.        ],\n",
       "        [ 0.43286133, -1.        ]], dtype=float32),\n",
       " 'label_components': array([[1., 0., 0.],\n",
       "        [1., 1., 0.],\n",
       "        [0., 1., 1.],\n",
       "        [1., 0., 0.],\n",
       "        [1., 0., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 0., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 0., 1.],\n",
       "        [1., 0., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 0., 1.],\n",
       "        [0., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 1., 1.],\n",
       "        [1., 0., 1.],\n",
       "        [1., 0., 0.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 0., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 1., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 0., 1.],\n",
       "        [1., 0., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 1., 0.],\n",
       "        [0., 1., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 1.],\n",
       "        [1., 1., 1.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [1., 1., 0.],\n",
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       "        [-2.6959186 ,  0.638835  ,  0.57963264,  1.        , -0.93013126,\n",
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       "        [ 2.7746851 ,  1.266077  ,  0.6697363 ,  1.1137097 ,  0.642927  ,\n",
       "          1.8280586 ,  0.26670828],\n",
       "        [-2.4563267 ,  1.9282122 ,  0.56301   ,  1.5623008 , -0.91291916,\n",
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       "        [-2.9112728 ,  0.60937154,  0.36277604,  1.        ,  0.        ,\n",
       "          0.6364526 ,  0.4646204 ],\n",
       "        [-2.21941   ,  0.6004722 ,  0.58599097,  1.        , -0.16797227,\n",
       "          0.99741626,  0.        ],\n",
       "        [-0.54870373,  1.8947555 ,  0.61795324,  1.7540423 , -0.5080704 ,\n",
       "          1.5980028 ,  1.3132071 ],\n",
       "        [-2.594077  ,  0.8970616 ,  0.44895026,  1.4177908 , -0.4855521 ,\n",
       "          1.2361134 ,  1.4266573 ],\n",
       "        [-0.15888265,  0.95004183,  0.16197932,  1.3298246 ,  0.        ,\n",
       "          0.6061892 ,  0.86046845],\n",
       "        [-0.87916535,  0.9445644 ,  0.80511063,  1.5436271 ,  0.4547054 ,\n",
       "          1.0863318 ,  0.        ],\n",
       "        [ 1.5825392 ,  0.77795225,  0.4798035 ,  1.        , -0.04237388,\n",
       "          1.3072939 ,  0.37675536],\n",
       "        [ 2.4138064 ,  1.9035664 ,  0.3673694 ,  1.        , -0.09113656,\n",
       "          1.187167  ,  0.5224555 ],\n",
       "        [-2.5251796 ,  0.6041114 ,  0.35622507,  1.        ,  0.        ,\n",
       "          1.8233935 ,  0.        ],\n",
       "        [-0.42892128,  1.3251876 ,  0.34107795,  1.3084366 , -0.27671412,\n",
       "          0.38398898,  0.03652378],\n",
       "        [-1.2647854 ,  0.4669409 ,  0.32771823,  1.        ,  0.        ,\n",
       "          0.38586137,  0.        ]], dtype=float32),\n",
       " 'generator_config': {'output_folder': 'data/lan_mlp/',\n",
       "  'dgp_list': 'glob',\n",
       "  'nbins': 0,\n",
       "  'n_samples': 1000,\n",
       "  'n_parameter_sets': 100,\n",
       "  'n_parameter_sets_rejected': 100,\n",
       "  'n_training_samples_by_parameter_set': 1000,\n",
       "  'max_t': 20.0,\n",
       "  'delta_t': 0.001,\n",
       "  'pickleprotocol': 4,\n",
       "  'n_cpus': 6,\n",
       "  'kde_data_mixture_probabilities': [0.8, 0.1, 0.1],\n",
       "  'simulation_filters': {'mode': 20,\n",
       "   'choice_cnt': 10,\n",
       "   'mean_rt': 15,\n",
       "   'std': 0,\n",
       "   'mode_cnt_rel': 0.5},\n",
       "  'negative_rt_cutoff': -66.77497,\n",
       "  'n_subruns': 10,\n",
       "  'bin_pointwise': False,\n",
       "  'separate_response_channels': False},\n",
       " 'model_config': {'name': 'glob',\n",
       "  'params': ['v', 'a', 'z', 'alphar', 'g', 't', 'theta'],\n",
       "  'param_bounds': [[-3.0, 0.3, 0.15, 1.0, -1.0, 1e-05, -0.0],\n",
       "   [3.0, 2.0, 0.85, 2.0, 1.0, 2.0, 1.45]],\n",
       "  'n_params': 7,\n",
       "  'default_params': [0.0, 1.0, 0.5, 2.0, 0.0, 1.0, 0.0],\n",
       "  'hddm_include': ['z', 'alphar', 'g', 'theta'],\n",
       "  'nchoices': 2,\n",
       "  'components': {'names': ['g', 'alphar', 'theta'],\n",
       "   'off_values': array([0., 1., 0.], dtype=float32),\n",
       "   'probabilities': array([0.33333333, 0.33333333, 0.33333333]),\n",
       "   'labels': array([[1, 0, 0],\n",
       "          [0, 1, 0],\n",
       "          [0, 0, 1]]),\n",
       "   'n_components': 3}}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting simulations\n",
      "simulations finished!\n",
      "simulations finished!simulations finished!\n",
      "\n",
      "simulations finished!\n",
      "simulations finished!simulations finished!\n",
      "\n",
      "simulations finished!\n",
      "simulations finished!simulations finished!\n",
      "\n",
      "simulations finished!\n",
      "simulations finished!\n",
      "simulations finished!\n"
     ]
    }
   ],
   "source": [
    "# NEEDS NEW CONFIG HERE\n",
    "my_output = my_dataset_generator.generate_data_ratio_estimator(save=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1., 0., 1., 1., 0., 0., 0.,\n",
       "       1., 0., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
       "       0., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_output[\"labels\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dataset_generator.model_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 512, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[\"data\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[\"labels\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         0.24663469,  1.0354215 ],\n",
       "       [-0.99073845,  1.3722177 ,  0.8484967 ,  1.2596086 ,  0.4464681 ,\n",
       "         1.9420478 ,  1.060621  ],\n",
       "       [-2.798319  ,  1.7204441 ,  0.57803786,  1.534741  ,  0.50243664,\n",
       "         1.963135  ,  1.0025971 ],\n",
       "       [ 1.3681768 ,  1.0043405 ,  0.30596825,  1.        ,  0.53021723,\n",
       "         0.10283308,  0.3134418 ],\n",
       "       [-2.1902065 ,  1.8060541 ,  0.6717961 ,  1.0324855 , -0.59285533,\n",
       "         1.2013143 ,  0.42820185],\n",
       "       [ 2.0757878 ,  1.8150367 ,  0.642386  ,  1.        , -0.04657958,\n",
       "         0.65097064,  0.5687484 ],\n",
       "       [ 0.4473417 ,  0.42394727,  0.47488734,  1.        ,  0.        ,\n",
       "         1.1451683 ,  0.        ],\n",
       "       [ 1.4074403 ,  0.47764823,  0.15800445,  1.135153  ,  0.        ,\n",
       "         0.22225042,  0.        ],\n",
       "       [-0.44654492,  1.9018015 ,  0.619462  ,  1.        ,  0.        ,\n",
       "         0.9219161 ,  0.7678479 ],\n",
       "       [-2.7916648 ,  1.9873564 ,  0.35208762,  1.8709652 ,  0.4222713 ,\n",
       "         0.95796293,  1.3054994 ],\n",
       "       [ 2.6202521 ,  1.0363461 ,  0.47605398,  1.        ,  0.        ,\n",
       "         1.3882989 ,  0.        ],\n",
       "       [-2.3632243 ,  1.319305  ,  0.36051476,  1.2646853 ,  0.        ,\n",
       "         1.6044955 ,  0.        ]], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_output[\"label_parameters\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "def normalized_gamma(t, alpha, tau, c):\n",
    "    num_ = np.power(t, alpha - 1) * np.exp(np.divide(-t, tau))\n",
    "    div_ = (\n",
    "        np.power(alpha - 1, alpha - 1) * np.power(tau, alpha - 1) * np.exp(-(alpha - 1))\n",
    "    )\n",
    "    return c * np.divide(num_, div_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.arange(0, 20, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in np.arange(1, 0.01, -0.01):\n",
    "    out = ssms.basic_simulators.gamma_drift(t=t, shape=10, scale=i, c=2)\n",
    "    plt.plot(t, out, alpha=i / 10, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1338b7a30>]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "out = ssms.basic_simulators.gamma_drift(t=t, shape=1.2, scale=0.5, c=2)\n",
    "plt.plot(t, out, alpha=5 / 10, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'normalized_gamma' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/OneDrive/project_ssm_simulators/ssm_simulators/basic_tutorial_tmp.ipynb Cell 29'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/afengler/OneDrive/project_ssm_simulators/ssm_simulators/basic_tutorial_tmp.ipynb#ch0000032?line=0'>1</a>\u001b[0m out \u001b[39m=\u001b[39m normalized_gamma(t \u001b[39m=\u001b[39m t, alpha \u001b[39m=\u001b[39m \u001b[39m10\u001b[39m, tau \u001b[39m=\u001b[39m \u001b[39m0.5\u001b[39m, c \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/afengler/OneDrive/project_ssm_simulators/ssm_simulators/basic_tutorial_tmp.ipynb#ch0000032?line=1'>2</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(t, out, alpha \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m, color \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mblack\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'normalized_gamma' is not defined"
     ]
    }
   ],
   "source": [
    "out = normalized_gamma(t=t, alpha=10, tau=0.5, c=2)\n",
    "plt.plot(t, out, alpha=1, color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x135e375b0>]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KRk62djB1mAZ9XwyMj+TbMzNZueMIe47o5CRKeZIGfT9tL7VfKDUlI9HaQvzQLbNGkhgdxm/f1slJlPIkDfp+2l5aS3xkKFkDdCCzvoqPDOO7l4zkg31Vn87MpZRyPw36ftp2uJYpGYk60chZumFGJmnxkfx21V6dnEQpD9Gg74em1nb2HWvgHG22OWuRYTbumD2abYdrWZ1/zOpylApIrkw8skJEKkVkdw/rvyEiO0Vkl4h8IiJTnNaVOJZvF5E8dxbuC3aV1dFpIGdYotWl+LVrzh3KiNQYfreqkA6dnEQpt3PliP5JYO4Z1h8EZhljJgG/BJZ3WX+pMSbHGJN7diX6ru2ltQBMGZpoaR3+LtQWwo/mZLO/spFXtpZZXY5SAafXoDfGrAOqz7D+E2NMjePhBuyTgAeFHWW1ZCRHMSA2wupS/N7ciWlMHprAH9/dT3ObDmOslDu5u41+CfCW02MDvCMiW0Rk6Zl2FJGlIpInInlVVVVuLsszth+uJSdDz593BxHhx3PHUl57imc2Hra6HKUCituCXkQuxR70P3ZaPNMYMxWYB9wqIhf3tL8xZrkxJtcYk5uamuqusjymsr6ZI3XN5GhHrNtcOCqFmaNSePj9IhqadRhjpdzFLUEvIpOBx4EFxphPT4g2xpQ7vlYC/wKmueP1fME2R/t8ToZONOJOd83NpvpkK499eNDqUpQKGP0OehEZBrwCXG+M2ee0PEZE4k7fB+YA3Z654492lNYSGiI6o5SbTR6ayJcmpfP4h8Ucb2yxuhylAoIrp1c+B6wHskWkTESWiMgyEVnm2OQeYADwly6nUQ4CPhKRHcAm4A1jzNse+B4ssb20lnHp8USG2awuJeDcOWcMLe2dPPSeDmOslDuE9raBMWZRL+tvAm7qZnkxMOXze/i/zk7DrrI65ucMtrqUgDQyNZbrzsvgmY2HufHCLLJSdHgJpfpDr4w9C4erm2hoaWfSEG228ZQ7Zo8mIjSE+9/aa3UpSvk9DfqzsKvcPqzuRA16jxkYF8myWSN5e89RNpf0eBmHUsoFGvRnYXd5HeG2EMYMirO6lIB200UjGBQfwa/eKNABz5TqBw36s7D7SB3ZaXGEh+rb50lR4Tb+a042O0preX1nhdXlKOW3NKn6yBjD7vJ6bbbxkqumDmVcejz3v72XlnYdGkGps6FB30dlNaeoO9WmHbFeYgsRfnrFOMpqTvH0J4esLkcpv6RB30f/6YiNt7iS4DFzdAqXZKfy0Jr91Da1Wl2OUn5Hg76PdpXXERoiZKdpR6w3/WTeOBpb2nloTZHVpSjldzTo+2h3eR1jBsUREapXxHpTdloc1+Zm8PT6Eg6dOGl1OUr5FQ36PrB3xNZp+7xF7rxsDKEhIdz/tl5EpVRfaND3QXntKWqa2pg4VIPeCgPj7RdRvbnrKBuKT/S+g1IK0KDvk93l9QBMHKwdsVZZevEIhiRG8T+v5ev8skq5SIO+D3aX12ELEcala9BbJSrcxk+uGEtBRT3Pb9aZqJRyhQZ9H+w+UsfogbE6NLHFvjQpnelZyfzfqkLqmnQmKqV6o0HvotMdsXpFrPVEhHu+PJ66U2388b19ve+gVJDToHfR0fpmjje26hk3PmLC4AQWThvG0+sPsf9Yg9XlKOXTXAp6EVkhIpUi0u1UgGL3oIgUichOEZnqtG6xiOx33Ba7q3Bvyz9i74idoB2xPuOHl40hJtzGfa/n6+iWSp2Bq0f0TwJzz7B+HjDacVsKPAIgIsnAL4Dp2CcG/4WIJJ1tsVY6HfRjtSPWZwyIjeCO2WP4cP9x3i2otLocpXyWS0FvjFkHnGn2hwXA08ZuA5AoIunA5cBqY0y1MaYGWM2Z/2D4rIKj9QwfEE1sRK+zLyovun7GcEYNjOVXb+Tr6JZK9cBdbfRDgFKnx2WOZT0t/xwRWSoieSKSV1VV5aay3KegooFxaXo072vCbCHcc+V4Dp1o4rF1xVaXo5RP8pnOWGPMcmNMrjEmNzU11epyPqOptZ2SEyf1/HkfdfGYVOZOSOOhNUWUVjdZXY5SPsddQV8OZDg9HupY1tNyv7L3aAPGwLh0HbHSV93z5fHYQoR7V+7RjlmlunBX0K8EbnCcfXM+UGeMqQBWAXNEJMnRCTvHscyvFFTYO2L1iN53DU6M4o7Zo3lvbyWr849ZXY5SPsXV0yufA9YD2SJSJiJLRGSZiCxzbPImUAwUAY8B3wUwxlQDvwQ2O273OZb5lYKKeuIiQxmaFGV1KeoMvnVhFtmD4vif1/Jpam23uhylfIZLp5AYYxb1st4At/awbgWwou+l+Y7THbEiYnUp6gzCbCH86qsT+dpf1/Pge0XcPW+s1SUp5RN8pjPWV3V2Ggoq6hmvF0r5hfMyk7nm3KE8/mGxXjGrlIMGfS8OVzfR1NqhHbF+5CfzxhITEcrPXt2tHbNKoUHfK+2I9T8DYiP48dyxbDxYzctb/e4kL6XcToO+FwUV9YQIjBmkR/T+ZOF5GZw7PIlfvZFPVUOL1eUoZSkN+l7kVzQwIlXHoPc3ISHC/VdPoqmlg3tf22N1OUpZSoO+FwUV9dps46dGDYzje18YxRs7K/TcehXUNOjPoO5UG+W1p7Qj1o/dMmskY9Pi+Nmru6hv1tmoVHDSoD+DvY6O2PF6RO+3wkNDuP/qyVQ1tPCbt/ZaXY5SltCgP4N8DfqAMCUjkW9fmMWzGw+zofiE1eUo5XUa9GdQUFHPgJhwUuMirC5F9dOdc8YwLDmau1/eSXObjluvgosG/RkUVDQwLl2HPggE0eGh/OaqSZScaOJ3qwqtLkcpr9Kg70F7RyeFxxq0IzaAXDAqhevPH84THx1k/QFtwlHBQ4O+BwePn6S1vVNPrQwwP7liLJkDovmvf+6gQc/CUUFCg74H+Tr0QUCKDg/l99dOoaLuFL96vcDqcpTyCg36HhRUNBBuC2FkaqzVpSg3O3d4MrfMGskLeaWs2asXUqnAp0Hfg4KKekYNjCU8VN+iQHTH7NGMTYvjrpd2UX2y1epylPIoV2eYmisihSJSJCJ3d7P+DyKy3XHbJyK1Tus6nNatdGPtHpWvQx8EtIhQGw9cm0PdqVZ+9uouHc5YBbReg15EbMDDwDxgPLBIRMY7b2OM+YExJscYkwM8BLzitPrU6XXGmPnuK91zjje2UNXQomfcBLjxg+O587Js3tx1lBfzSq0uRymPceWIfhpQZIwpNsa0As8DC86w/SLgOXcUZ5UCvSI2aNxy8QguHDWAe1fmU1SpM1KpwORK0A8BnA93yhzLPkdEhgNZwBqnxZEikiciG0TkKz29iIgsdWyXV1VV5UJZnqOTjQSPkBDhgWtziAq38b3ntutVsyogubuncSHwkjHG+bdluDEmF/g68EcRGdndjsaY5caYXGNMbmpqqpvL6puCigbS4iNJigm3tA7lHYPiI/m/r02moKJeBz5TAcmVoC8HMpweD3Us685CujTbGGPKHV+LgbXAOX2u0st0MvDg84Wxg/jWhZk8+UkJ7+rY9SrAuBL0m4HRIpIlIuHYw/xzZ8+IyFggCVjvtCxJRCIc91OAC4F8dxTuKS3tHRRVNmpHbBC6e95YxqfH86OXdnC0rtnqcpRym16D3hjTDtwGrAIKgBeNMXtE5D4RcT6LZiHwvPnseWrjgDwR2QG8D/zGGOPTQV9U2Uh7p9H2+SAUEWrjoa+fQ0t7J7c9u5W2jk6rS1LKLUJd2cgY8ybwZpdl93R5fG83+30CTOpHfV6Xf0Q7YoPZyNRYfn3VJL7//HZ+89Zefn7l+N53UsrH6WWfXRRUNBAZFkLmgBirS1EWWZAzhBsvyOSJjw7yxs4Kq8tRqt806LsoqKgnOy0eW4iOQR/M/vuKcUwdlshdL+2gqLLR6nKU6hcNeifGGAqO1jNeO2KDXnhoCA9/YyqRYTaW/WMLJ1varS5JqbOmQe/kaH0ztU1t2j6vAEhPiOKhRedQXNXIXS/v1PFwlN/SoHeiV8Sqri4YlcJdc8fyxs4KHn6/yOpylDorLp11EywKKuxjnYxN06Yb9R+3XDyCwqMN/N87+xg1MI65E9OsLkmpPtEjeif5FfVkJEcRFxlmdSnKh4gIv75qEjkZifzghe3sOVJndUlK9YkGvZOCinrGpWmzjfq8yDAby284l8ToMG5+Ko+qhharS1LKZRr0DqdaOyg5flLb51WPBsZF8tgNuVQ3tbLsH1t0pEvlNzToHQqPNdBp0DFu1BlNHJLA77+Ww5ZDNfzwnzvo7NQzcZTv085YBz3jRrnqS5PTKasZy6/f2kt6fCQ/02ESlI/ToHcoqKgnJtxGRlK01aUoP7D04hFU1DXz+EcHSU+MYsnMLKtLUqpHGvQOBRX1jE2PJ0SHPlAuEBF+fuV4jtY186s38kmLj+RLk9OtLkupbmkbPfahD/ZWNGj7vOoTW4jwx4U5nDssiR+8sJ0NxSesLkmpbmnQA2U1p2hoadf2edVnkWE2Hl+cy7AB0dz0VB47SmutLkmpz3Ep6EVkrogUikiRiNzdzfobRaRKRLY7bjc5rVssIvsdt8XuLN5d8rUjVvVDYnQ4/1gynaSYMG5YsYm9R+utLkmpz+g16EXEBjwMzAPGA4tEpLvTDF4wxuQ4bo879k0GfgFMB6YBvxCRJLdV7yYFFfWI6NAH6uylJUTy7E3nExVm45uPb6K4Soc2Vr7DlSP6aUCRMabYGNMKPA8scPH5LwdWG2OqjTE1wGpg7tmV6jkFFfVkDoghOlz7ptXZy0iO5h83TccYwzcf30hZTZPVJSkFuBb0Q4BSp8dljmVdXS0iO0XkJRHJ6OO+iMhSEckTkbyqqioXynKfAu2IVW4yamAsf18yncaWdhY9tkHDXvkEd3XGvgZkGmMmYz9qf6qvT2CMWW6MyTXG5KamprqprN41NLdxuLpJx7hRbjN+cDx/XzKduqY2rnt0A4dPaNgra7kS9OVAhtPjoY5lnzLGnDDGnB7l6XHgXFf3tVrhUfvQxNoRq9xpSkYiz958Pidb27lu+XpKjp+0uiQVxFwJ+s3AaBHJEpFwYCGw0nkDEXG+UmQ+UOC4vwqYIyJJjk7YOY5lPuPToQ8Ga9Ar95o4JIFnbzqflvZOrn10PQe0g1ZZpNegN8a0A7dhD+gC4EVjzB4RuU9E5js2u11E9ojIDuB24EbHvtXAL7H/sdgM3OdY5jPyKxqIjwxlcEKk1aWoADR+cDzP3Xw+ncZw3aMbdCx7ZQnxxXkwc3NzTV5enldea/6fPyI2IpRnbz7fK6+nglNRZSM3PLGRhuZ2lt+Qy4yRA6wuSQUYEdlijMntbl1QXxnb1tHJ3ooGJg5JsLoUFeBGDYzlpe9cQFpCJItXbOKtXRVWl6SCSFAH/f5jjbR2dDJB2+eVFwxOjOKfy2YwcUg83312K//YcMjqklSQCOqg3+1oL9UjeuUtidHhPHPT+VyaPZCfvbqb+9/eq5OXKI8L6qDfU15HTLiNrAExVpeigkhUuI1Hrz+XRdOG8cjaA3znmS00tbZbXZYKYEEd9LuP1DN+sI5Br7wvzBbC/351Ij+/cjyr84/xtb+u52hds9VlqQAVtEHf0WnIP1LPhMHabKOsISIsmZnFE4vP49CJJub/+SN2ltVaXZYKQEEb9AePN3KqrUPb55XlLh07kJe/cwHhoSFc89f1PL/pML542rPyX0Eb9LvL7VfEThyiZ9wo62WnxbHytplMz0rm7ld2cddLO2lu67C6LBUggjjo64gIDWFUaqzVpSgFQHJMOE9+axq3f3E0/9xSxlV/+YRDJ3SMHNV/wRv0R+oYmx5PqC1o3wLlg2whwp2XjeFvN55Hee0prnzoI17bccTqspSfC8qU6+w07CmvZ6JeKKV81KVjB/L692YyMjWW7z23jTtf3E5ji56Cqc5OUAZ9aU0TDS3tesaN8mkZydH8c9kMbv/CKF7dVs4Vf/qQrYdrrC5L+aGgDPrtpbUATB6qQa98W5gthDvnZPPCLTPo6DR87a/r+f07hbS0a0etcl3QBn1kWIhOBq78xnmZybx1x0UsmDKYh9YUceWDH7FNj+6Vi4Iy6HeU1jJpSIJ2xCq/Eh8ZxgPX5bDixlwamtu5+pFP+N83CzjVqkf36syCLula2zvZfaSeKUMTrS5FqbPyhbGDeOfOi7nuvGEsX1fMvD+tY21hpdVlKR/mUtCLyFwRKRSRIhG5u5v1d4pIvojsFJH3RGS407oOEdnuuK3suq+37T1aT2t7JznDEq0uRamzFh8Zxq+vmsSzN08nRIQb/7aZm5/Oo7RaJyJXn9dr0IuIDXgYmAeMBxaJyPgum20Dco0xk4GXgN86rTtljMlx3OZjsR2Ojlg9oleB4IKRKbx1x0X8eO5YPi46zuwHPuCP7+7Tq2rVZ7hyRD8NKDLGFBtjWoHngQXOGxhj3jfGnD6U2AAMdW+Z7rOttJaU2HCGJkVZXYpSbhERauM7l4zkvR/OYvb4Qfzx3f188fcf8MrWMh3rXgGuBf0QoNTpcZljWU+WAG85PY4UkTwR2SAiX+lpJxFZ6tgur6qqyoWyzs6O0lpyMhIR0aGJVWBJT4ji4a9P5dmbp5MYHcadL+7gSw99xAf7qnSQtCDn1s5YEfkmkAv8zmnxcMeEtV8H/igiI7vb1xiz3BiTa4zJTU1NdWdZn6o71caBqpPabKMC2gUjU3jttpn8aWEOjS1tLF6xiW88vlFPxwxirgR9OZDh9HioY9lniMhs4KfAfGNMy+nlxphyx9diYC1wTj/q7ZfTVxVOHZ5kVQlKeUVIiLAgZwjv3jmLX3x5PHuPNvDVv3zC9U9sZHNJtdXlKS9zJeg3A6NFJEtEwoGFwGfOnhGRc4BHsYd8pdPyJBGJcNxPAS4E8t1VfF9tPlhNaIhwjp5xo4JERKiNb12Yxbq7LuXueWPJP1LP1/66nkXLN/DJgePapBMkQnvbwBjTLiK3AasAG7DCGLNHRO4D8owxK7E31cQC/3S0fR92nGEzDnhURDqx/1H5jTHGuqAvqWbCkASiw3v9tpUKKLERoSybNZIbZgzn2Y2HeXRdMV9/bCNTMhJZMjOLeRPTCNMLCAOW+OJf9NzcXJOXl+fW52xu62Dyve+w+ILh/PRLXc8OVSq4NLd18M+8UlZ8XMLB4ydJT4jkxgsyWThtGAlRYVaXp86CiGxx9Id+TtAc2u4sq6O1o5PzMpOtLkUpy0WG2bh+RibfmD6cNXsreeKjg/z6rb386b39LMgZwtenDWOSDvoXMIIm6E93QGnQK/UfISHC7PGDmD1+EHuO1PHkxyX8a1sZz206zITB8SyaNowFOYOJi9SjfH8WNE03N/5tE+U1p1h95yy3Pq9SgabuVBsrt5fz7KZSCirqiQqzMW9SGl/JGcIFIwfoYIA+KuibblrbO9l0sJqrp/rsBbtK+YyEqDCun5HJN88fzs6yOp7bdJg3dlbwytZyUmIjuHJyOgtyBuuFh34kKIJ+6+Eamlo7uGh0itWlKOU3RIQpGYlMyUjk3vkTWFtYyb+3H+HZTYd58pMShiVHM3diGnPGD+KcYUnYQjT0fVVQBP1H+49jCxHOHznA6lKU8kuRYTbmTkxn7sR06pvbeHv3UV7bcYS/fXyQ5euKSYkNZ/a4QcyZMIgLRqYQGWazumTlJCiC/sP9VZyTkUi8digp1W/xkWFcm5vBtbkZ1De3sbawinf2HOX1nRU8v7mUqDAb00ckc9HoVC4encKogbHaxGOxgA/6mpOt7Cyv444vjrG6FKUCTnxkGPOnDGb+lMG0tHewobiaNQXH+LDoOL983X5tZFp8JDNHp3DR6BSmZSWTnqAjx3pbwAf9+4WVGAOzsj0zUJpSyi4i1MasManMGmP/XSuraeKj/cf5cP9xVucf46UtZQAMTYpiWmYyuZnJTMtKYmSqHvF7WsAH/du7j5IWH8nkIXrxh1LeNDQpmoXThrFw2jA6Og35R+rZXFLN5pJq1u2v4pVt9rERk2PCyclIZNKQBCYPTWDS0AQGxkVaXH1gCeigb2ptZ93+Kq7LzSBEzwhQyjK2EGGSI8S/PTMLYwwHj590BH8NO8tqWVtYyel5UtLiI+3bD0lgXHo82YPiGJoUpb/HZymgg/6Dwiqa2zq5fEKa1aUopZyICCNSYxmRGst15w0D4GRLO/kV9ewsq2NXWS07y+tYnX/s032iw22MHhRH9qBYxgyKIzstjlEDYxkUF6l/AHoR0EH/0pYyBsZFMC1Lhz1QytfFRIRyXmbyZ4YpaWhuY39lI/uONlB4rIHCow2s2VvJi3lln24TGRbC8OQYMlOiyUyJIWtAjP1rSgwD4yK0/Z8ADvpj9c28X1jJLbNG6iXbSvmpuMgwpg5LYuqwz04WdLyxhX3HGiiuOknJ8ZOUnDhJUWUj7++torWj89PtwkNDGJwQyZCkKAYnRDE4MYohiVH2x4lRpCdEBsU5/wEb9M9vKqXTwLW5Gb1vrJTyKymxEaTERnDByM9e7d7RaThSe4qDjvAvrzlFea39tm5/FZUNLXQd3ishKozUuAhSYyMYGG//mhpnvw2MiyQ1LoIBseEkRoX57UFjQAZ9Y0s7f/vkILPHDSQrJcbqcpRSXmILETKSo8lIjuZiPn9KdWt7J8fqmymrOcWRWvutqrGFqoYWKhta2Ha4lsqGZprbOrt5doiLDCUpOpyk6DASo8NJjA4jqcvXhKgw4iJDiY0IIzYylNgI+83KISJcCnoRmQv8CfsMU48bY37TZX0E8DRwLnACuM4YU+JY9xNgCdAB3G6MWeW26nvwyNoiapvauPXSUZ5+KaWUHwkPDfn0D0FPjDGcbO2gsr6ZqoYWqhpbONHYSk1TK7VNbdQ0tVLT1EZtUysHj5+kpqmVhub2Xl87Jtz2n+CPDCMu4vT9UKLDbUSHh5IcE8bSi0e681sGXAh6EbEBDwOXAWXAZhFZ2WVKwCVAjTFmlIgsBO4HrhOR8djnmJ0ADAbeFZExxpgOd38jp324v4pHPyjm6qlDOWeYTgKulOobEbEHsOOsIFe0d3RSe8oe/vXN7TQ0t9PY3E5jS5v9fov98en7DS3tNDa3UdnQTGNzO01tHTS1dJBkVdAD04AiY0wxgIg8Dyzgs5N8LwDuddx/Cfiz2Lu6FwDPG2NagIMiUuR4vvXuKf8/Wto7uPbRDewsq2XMwDju+bJOF6iU8o5QW8in/Qb90d7RfZNRf7kS9EOAUqfHZcD0nrZxTCZeBwxwLN/QZd8h3b2IiCwFlgIMGzbMldo/IyLUxoiUGC4Zk8pNF2XpjDhKKb/jqc5en+mMNcYsB5aDfYaps3mOP1yX486SlFIqILjy56MccD5HcahjWbfbiEgokIC9U9aVfZVSSnmQK0G/GRgtIlkiEo69c3Vll21WAosd968B1hj7ZLQrgYUiEiEiWcBoYJN7SldKKeWKXptuHG3utwGrsJ9eucIYs0dE7gPyjDErgSeAvzs6W6ux/zHAsd2L2Dtu24FbPXnGjVJKqc8T0/UyMR+Qm5tr8vLyrC5DKaX8hohsMcbkdrfOP6/nVUop5TINeqWUCnAa9EopFeA06JVSKsD5ZGesiFQBh85y9xTguBvLcRetq2+0rr7RuvomEOsaboz5/JCd+GjQ94eI5PXU82wlratvtK6+0br6Jtjq0qYbpZQKcBr0SikV4AIx6JdbXUAPtK6+0br6Ruvqm6CqK+Da6JVSSn1WIB7RK6WUcqJBr5RSAc5vg15E5opIoYgUicjd3ayPEJEXHOs3ikimF2rKEJH3RSRfRPaIyPe72eYSEakTke2O2z2ersvxuiUissvxmp8bMU7sHnS8XztFZKoXasp2eh+2i0i9iNzRZRuvvF8iskJEKkVkt9OyZBFZLSL7HV+7nYRYRBY7ttkvIou728bNdf1ORPY6fk7/EpHEHvY948/cA3XdKyLlTj+rK3rY94y/ux6o6wWnmkpEZHsP+3ry/eo2G7z2GTPG+N0N+3DJB4ARQDiwAxjfZZvvAn913F8IvOCFutKBqY77ccC+buq6BHjdgvesBEg5w/orgLcAAc4HNlrwMz2K/aIPr79fwMXAVGC307LfAnc77t8N3N/NfslAseNrkuN+kofrmgOEOu7f311drvzMPVDXvcB/ufBzPuPvrrvr6rL+98A9Frxf3WaDtz5j/npE/+mE5caYVuD0hOXOFgBPOe6/BHxRRMSTRRljKowxWx33G4ACepgj1wctAJ42dhuARBFJ9+LrfxE4YIw52yui+8UYsw77XArOnD9DTwFf6WbXy4HVxphqY0wNsBqY68m6jDHvGGPaHQ83YJ+5zat6eL9c4crvrkfqcvz+Xws8567Xc9UZssErnzF/DfruJizvGqifmbAcOD1huVc4morOATZ2s3qGiOwQkbdEZIKXSjLAOyKyRewTsXflynvqSQvp+RfQivcLYJAxpsJx/ygwqJttrH7fvo39P7Hu9PYz94TbHE1KK3pohrDy/boIOGaM2d/Deq+8X12ywSufMX8Nep8mIrHAy8Adxpj6Lqu3Ym+emAI8BLzqpbJmGmOmAvOAW0XkYi+9bq/EPkXlfOCf3ay26v36DGP/H9qnzkUWkZ9in7ntmR428fbP/BFgJJADVGBvJvElizjz0bzH368zZYMnP2P+GvT9mbDco0QkDPsP8hljzCtd1xtj6o0xjY77bwJhIpLi6bqMMeWOr5XAv7D/C+3Myonc5wFbjTHHuq6w6v1yOHa6+crxtbKbbSx530TkRuBK4BuOgPgcF37mbmWMOWaM6TDGdAKP9fB6Vr1focBVwAs9bePp96uHbPDKZ8xfg74/E5Z7jKMN8AmgwBjzQA/bpJ3uKxCRadh/Bh79AyQiMSISd/o+9s683V02WwncIHbnA3VO/1J6Wo9HWla8X06cP0OLgX93s80qYI6IJDmaKuY4lnmMiMwF7gLmG2OaetjGlZ+5u+ty7tP5ag+v58rvrifMBvYaY8q6W+np9+sM2eCdz5gnepi9ccN+lsg+7D34P3Usuw/7hx8gEntTQBGwCRjhhZpmYv/Xayew3XG7AlgGLHNscxuwB/vZBhuAC7xQ1wjH6+1wvPbp98u5LgEedryfu4BcL/0cY7AHd4LTMq+/X9j/0FQAbdjbQJdg79N5D9gPvAskO7bNBR532vfbjs9ZEfAtL9RVhL3N9vRn7PTZZYOBN8/0M/dwXX93fHZ2Yg+w9K51OR5/7nfXk3U5lj95+jPltK0336+essErnzEdAkEppQKcvzbdKKWUcpEGvVJKBTgNeqWUCnAa9EopFeA06JVSKsBp0CulVIDToFdKqQD3/wE8RLnlbmbwfQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(100).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "model = \"gamma_drift_angle\"\n",
    "params_tmp = ssms.config.model_config[model][\"default_params\"]\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"c\")] = 3.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"theta\")] = 0.0\n",
    "\n",
    "sim_out = simulator(model=model, theta=params_tmp, n_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "model = \"angle\"\n",
    "params_tmp = ssms.config.model_config[model][\"default_params\"]\n",
    "\n",
    "sim_out = simulator(model=model, theta=params_tmp, n_samples=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_out[\"rts\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0, 0.5, 0.25, 5.0, 0.5, 1.0]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssms.config.model_config[\"gamma_drift\"][\"default_params\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.000e+00, 2.000e+00, 4.000e+00, 3.900e+01, 9.600e+01, 2.440e+02,\n",
       "        4.570e+02, 6.730e+02, 8.610e+02, 2.370e+02, 0.000e+00, 0.000e+00,\n",
       "        2.800e+01, 6.280e+02, 9.300e+02, 9.120e+02, 9.600e+02, 1.102e+03,\n",
       "        1.027e+03, 8.510e+02, 5.290e+02, 2.670e+02, 9.800e+01, 3.800e+01,\n",
       "        9.000e+00, 3.000e+00, 2.000e+00, 1.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([-2.27103591, -2.06870387, -1.86637184, -1.6640398 , -1.46170777,\n",
       "        -1.25937573, -1.0570437 , -0.85471166, -0.65237962, -0.45004759,\n",
       "        -0.24771555, -0.04538352,  0.15694852,  0.35928055,  0.56161259,\n",
       "         0.76394463,  0.96627666,  1.1686087 ,  1.37094073,  1.57327277,\n",
       "         1.7756048 ,  1.97793684,  2.18026888,  2.38260091,  2.58493295,\n",
       "         2.78726498,  2.98959702,  3.19192905,  3.39426109,  3.59659313,\n",
       "         3.79892516]),\n",
       " <BarContainer object of 30 artists>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.hist(sim_out[\"rts\"] * sim_out[\"choices\"], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7386"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sim_out[\"choices\"] == 1).sum() / sim_out[\"choices\"].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test new tradeoff model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAV6UlEQVR4nO3dUYxk113n8e9/uxMgIRo7ZGSFmVHVSFhBBolN1HLMWkKrGBITEOOHCHnFhlHklV8CxCYSm+xLZgGtghRhhwciWR5Yw0YxkYnkESCylhMJIYFJT4xCbJP1iKmOZ7DjgfEMXtACPfz3oc6dVNdUVVd3V9et6vP9SKW6de+t6nNL6t89de6550RmIkmqw79ruwCSpPkx9CWpIoa+JFXE0Jekihj6klSR1bYLMMnb3va27Ha7bRdDkpbK2bNn/y4zD4/attCh3+12WV9fb7sYkrRUImJj3DabdySpIoa+JFXE0Jekihj6klQRQ1+SKmLoS1JFDH1JqoihL0kVMfQlqSKG/pS63S4Rse3DYSMkLbKFHoZhkWxsbDDNLGMRMYfSSNLuWNOXpIoY+pJUEUNfkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKTBX6EfFgRDwXEV+PiM9FxHdGxPGIeCYizkXE70XEG8u+31FenyvbuwOf8/Gy/hsR8b59OiZJ0hjbhn5EHAF+AVjLzB8EVoB7gV8DHsrM7wNeA+4rb7kPeK2sf6jsR0TcVt73A8DdwG9GxMpsD0eSNMm0zTurwHdFxCrwJuBl4D3AE2X7Y8A9ZflEeU3Zflf0B6Q5ATyemf+cmeeBc8Dtez4CSdLUtg39zLwIfAr4Jv2wvwqcBa5k5mbZ7QJwpCwfAV4q790s+3/P4PoR77kuIu6PiPWIWL906dJujkmSNMY0zTs306+lHwe+F3gz/eaZfZGZj2TmWmauHT58eL/+jCRVaZrmnR8Fzmfmpcz8V+ALwJ3ATaW5B+AocLEsXwSOAZTth4C/H1w/4j2SpDmYJvS/CdwREW8qbfN3Ac8DXwY+UPY5CTxZls+U15TtX8r+QPRngHtL757jwK3AX8zmMPZmmglSACdOkbT0tp1EJTOfiYgngK8Cm8CzwCPAHwKPR8SvlnWny1tOA78bEeeAy/R77JCZz0XE5+mfMDaBD2fmtRkfz65sN0FKRGy7XZKWQUwzG1Rb1tbWcn19fd//zixCvXn/dp8lSfstIs5m5tqobd6RK0kVMfQnaNr6J+l0OnMqjSTtnaE/wcbGxrahvrGxMafSSNLebXsht3ZNqI9rp/cirqRlYk1fkipi6EtSRQx9SaqIoS9JFTH0Jakihr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0J/SdhOiTzOBuhOpS2qb4+lPadR4+hFBp9OZOJFKp9Oh1+ttmTvXMfgltcXQn9JwUDczag0H+nbvk6Q22bwzwnZz425Xu5ekRWVNf4SNjQ0yc0vwD9bkrb1LWlbW9CWpItb0Z2RS7d9fBpIWhTX9GWgu6krSorOmXwzXxndSO+/1elN/riS1yZp+kZnXH81rSTpoqg/95u7YwTtmm9fb6XQ63nUraalU37zT9Lcf7pI53GVzFJt1JC2b6mv6u9FcuLWGL2nZVF/Tb0y6kDvcO2fS0AvW8CUtMmv622gGTJOkg8DQL8b13jHwJR0kNu/sUtNzZxTb9SUtKkN/jMGunDvlCJySFpWhP8aorpzT8EKupEVmm74kVcTQl6SKTBX6EXFTRDwREX8dES9ExA9HxFsj4qmIeLE831z2jYj4jYg4FxFfi4h3DXzOybL/ixFxcr8OSpI02rQ1/U8Df5yZ3w/8EPAC8DHg6cy8FXi6vAb4ceDW8rgf+AxARLwV+ATwbuB24BPNiaItg71shsfekaSDaNvQj4hDwI8ApwEy818y8wpwAnis7PYYcE9ZPgH8Tvb9OXBTRLwdeB/wVGZezszXgKeAu2d4LDs22MtmuJ++JB1E09T0jwOXgN+OiGcj4tGIeDNwS2a+XPZ5BbilLB8BXhp4/4Wybtz6LSLi/ohYj4j1S5cu7exoJEkTTRP6q8C7gM9k5juBf+TbTTkAZL96PJMqcmY+kplrmbl2+PDhWXykJKmYJvQvABcy85ny+gn6J4FvlWYbyvOrZftF4NjA+4+WdePWS5LmZNvQz8xXgJci4h1l1V3A88AZoOmBcxJ4siyfAX629OK5A7hamoG+CLw3Im4uF3DfW9ZJkuZk2jtyfx74bES8Efgb4EP0Txifj4j7gA3gp8u+fwS8HzgH/FPZl8y8HBG/Anyl7PfLmXl5JkchSZpKLHJvlbW1tVxfX9+3zx/snjk8c9ao9bv5zFGfNW4sfkmahYg4m5lro7Z5R64kVcTQl6SKGPqSVBFDX5Iq4nj6hWPuSKqBoV+M670jSQeJzTv7aPjkMTiKZ7PsfLqS5sma/j4a7JffvB5cHtwmSfNgTV+SKmLoS1JFDP0Z63Q615eHZ+IaXrZ9X9K8Gfoz1uv1ri9POxNXp9MhM7fM5CVJ+8HQ3wdNbX/SnLtN0AOGvaS5MfT3Qa/Xu2HO3cFmH+gH/eAJwaYdSfNg6BdNrXwwiIeDei8Gm30ag00/1vYlzYP99ItRd+SOCmpJWmbW9Oeg0+mMbNsft87ePJL2izX9OWh+MQyH/PDMWs26hnfrSpo1a/qSVBFDX5IqYuhLUkUMfUmqiKEvSRUx9CWpIoa+JFXE0JekinhzVjF8I9Qsx92RpEVRbU1/0hAHnU7HcXckHUjV1vSHR7WcZrITSVp21db0JalGhr4kVcTQn7PhidMlaZ4M/TkbnjhdkubJ0J+j4clUrOlLmjdDf46aCdMb1vQlzZuh34KmXX9UTd8pEiXtJ0O/BU27/qia/vD9A5I0S9XenNW2cZOlQ/8XgMNASNoPhn5Lxk2WDqMnTJekWZi6eSciViLi2Yj4g/L6eEQ8ExHnIuL3IuKNZf13lNfnyvbuwGd8vKz/RkS8b+ZHI0maaCdt+h8BXhh4/WvAQ5n5fcBrwH1l/X3Aa2X9Q2U/IuI24F7gB4C7gd+MiJW9FV+StBNThX5EHAV+Ani0vA7gPcATZZfHgHvK8onymrL9rrL/CeDxzPznzDwPnANun8ExSJKmNG1N/2Hgl4B/K6+/B7iSmZvl9QXgSFk+ArwEULZfLftfXz/iPddFxP0RsR4R65cuXZr+SJbUqAu2wzdw2Y1T0qxsG/oR8ZPAq5l5dg7lITMfycy1zFw7fPjwPP5kq0aN2z98A5fdOCXNyjQ1/TuBn4qIHvA4/WadTwM3RUTT++cocLEsXwSOAZTth4C/H1w/4j0aMGqoBmv8kmZh29DPzI9n5tHM7NK/EPulzPwZ4MvAB8puJ4Eny/KZ8pqy/UvZr7qeAe4tvXuOA7cCfzGzI1li0/bJt8Yvaa/20k//vwKPR8SvAs8Cp8v608DvRsQ54DL9EwWZ+VxEfB54HtgEPpyZ1/bw9w+MXq+3pXY/2E/fPvuSZikWedCvtbW1XF9f35fPHg7Str+HaUO/7XJKWnwRcTYz10Ztc+ydBeTwy5L2i6G/gBx+WdJ+MfQlqSKG/gIbNRJnRGx52I1T0k44yuYCGzUS53Bzj23+knbCmr4kVcTQl6SKGPqSVBFDX5IqYuhLUkUM/QXSDLw2OLKmvXMkzZKhv0CaLppNt8zM9I5cSTNlP/0FMXgj1vCzJM2KNf0F0ev1ttTwm2dr+pJmydBfMMM1/uHavsMuSNoLQ3/BDNf4h2v6zp4laS8MfUmqiKG/oJpmnlEXc23ikbRb9t5ZUE33TbixF49NPJJ2y5q+JFXE0Jekihj6S2qwvd82fknTMvSX1GB3Ttv4JU3L0Jekihj6S2q4O6dNPJKmYegvqeG7dW3ikTQNQ39JjbpxKyKs8UuayNBfIoM1+3EjcFrjlzSJoS9JFTH0Jakihv4SGJ47t1l2Zi1JO2XoL4Fer3c9+CVpLwz9JTE4uQo4laKk3TH0Jakihv6SGWzft01f0k4Z+kummVxlUvNOc0IYfHjTliRw5qwDadTJwF8FksCaviRVxdBfQpMmTQdH3JQ03rahHxHHIuLLEfF8RDwXER8p698aEU9FxIvl+eayPiLiNyLiXER8LSLeNfBZJ8v+L0bEyf07rIOt6b45rk3f8XckjTNNTX8T+Ghm3gbcAXw4Im4DPgY8nZm3Ak+X1wA/DtxaHvcDn4H+SQL4BPBu4HbgE82JQpI0H9uGfma+nJlfLcuvAy8AR4ATwGNlt8eAe8ryCeB3su/PgZsi4u3A+4CnMvNyZr4GPAXcPcuDGaXb7Y7szXKQjLpb1547kkbZUe+diOgC7wSeAW7JzJfLpleAW8ryEeClgbddKOvGrd833W53qqaOZR/ioNfr3XAiG2z6OWgnOUm7N/WF3Ij4buD3gQcy8x8Gt2U/YWYyJkBE3B8R6xGxfunSpT19VhP4Tfv34GNQ0/f9IDmov2ok7c1UoR8Rb6Af+J/NzC+U1d8qzTaU51fL+ovAsYG3Hy3rxq3fIjMfycy1zFw7fPjwTo5FTP7VYnOPpGl67wRwGnghM399YNMZoOmBcxJ4cmD9z5ZePHcAV0sz0BeB90bEzeUC7nvLun13kNv0m+6bje2aszqdjr17pIpN06Z/J/BB4K8i4i/Luv8GfBL4fETcB2wAP122/RHwfuAc8E/AhwAy83JE/ArwlbLfL2fm5VkcxHYO8h2qTdPUNMdj4EuKRR6ed21tLdfX13f9/iYItwv9Rf4OpjV4rBFxw/Owg3DMkkaLiLOZuTZqm3fkSlJFqgj9SW36KysrLZZsdoanVBx+liSoJPQnddnc3NxssWSz45SKkqZRRejXohmTx/CXNI6hfwANzqfrBVtJgwx9SaqIoV8pp1SU6uR0iZU6yDesSRqvitA3zCSpr4rQt1YrSX1VhH6thgdjGzS8/qDcpCZpMkP/ANtuMDYnWpHqU0XvnYM8tPI0xt2sNer7sAePdLBVEfrbzZx10DU1/uFjH3UDl0MvSwdbFaFfe01/nHGDstlnXzq4qm7Tr2mMmkkXdYeNG4Nf0vKroqY/rnnnIE6IPk4zHs80A7JZy5cOripCX1sNtvEPPjds15cOLkNfkipi6EtSRQz9Sg1e2J10166jcEoHi6Ffqd1MtLKxsWH4S0vO0Bcwufvq4LbM9EKvtMSq6Kc/qvmipj76k0zTf38w5O2/Ly23KkK/tmEXdmLUvQrTBHu3263qPgfpoLB5RyNtN0aRTTzScjL0Jakihv4E3S5EjH/U0Ill0rUPe/FIy8fQH6EJ++1aMDY2DubJoLm4GxETm3GaLpyjHp4QpMVk6I+wsQF76dyz7M3dg4OzDT6m6fHU7Gubv7SYDP0xJmXWES6QBB16Y/eJgG6M376Mmt46k8J/cL4Ca/vS4qk+9FdXb2ye2c5FjhIkG3Qn7rdB98BdE1hZWZlYix88IVjblxZP9aF/7Rpklsc2tfdprLylR9I/cyQx9nGe7pZrAt3VCzM4mv23ubm5pTvncLPPcNBb25cWS9Wh3+RRBMSD/Rcja++HenAqOH9ofIg3j2uv99/fBP+wXvR/JXTZGo4b144u3S+B5oLvTi72ehKQ2lXFHbmjdLtD7fYP96AJ6kM9ePD4je+5CnFqmw8+NWGfKx3y4f4f7dEZe2Jo9DY6EAOF7HRgge6CHb4jNyLodDpTnQQaKysrbG5u7lcRJQ2pNvRH5VKPfjNFXj1O9wps3LR1+7aBD+VXwfg7WZu4O06PDj163HhyufAWOPZRyFMbQ39zA/57/xM6hzr0HuhNUaD5mRT4g9uaOXidi1eav1jkcWnW1tZyfX191+9vAmXUMY7NmoFa/iyDtftwl42rQ4F4KuFUKciVDtxUQvHUjSeYzhXoPXzj5/YOwX/80IhtC/KroNvt7uiCbqfTcUwfaY8i4mxmro3cVn3onxqd/vOoSd/QxNQY07w0bNyJAMrJ4NTi/BrYafgP82QgTc/Q73RuSNegHPepuF6zbp7bbjoZezIYZcwJonPlxuapzhX409Nw9PX+SeH4g9B5fYXep+bfpr7bk4DhL22v+tBfect5/t//Pc7qwKEGSRLXw/66Bf4+BnUf7rLxi+cgb7wss8Imb3/wDVw4NPq9o34hNCeBkfvP8cSwurrKtWvXdvVeTwhS30KFfkTcDXwaWAEezcxPjtt3VqF/hJe4wLFv1+6LDj02Th3n/EOL1RSyUzv6ZQDwQPf69YOdOHIVVnPrCWPsNQXY83WFvTYJbS2KJwTVY2FCPyJWgP8D/BhwAfgK8J8y8/lR+88m9M/DuL73+3DBdpGtrvZvRpvW+ThGN/s3jXUfuLG5aBpHrsLFgV8ck65DjDTmxDHLE8LoP+tJQstrUujPu8vm7cC5zPwbgIh4HDgBjAz92ehuabdv9IN+OZpyZmXa7vDNL4fj+dK3Vz68u795cej1Btx4d8LEC9ff7qa6xYd2Vx4eAq5uv9vw/QTz5klH+2XeNf0PAHdn5n8prz8IvDszf25gn/uB+8vLdwDfmFsBZ+NtwN+1XYiW+R34HYDfAbT3HXQy8/CoDQt3c1ZmPgI80nY5disi1sf9rKqF34HfAfgdwGJ+B/Mee+cicGzg9VFubAGQJO2TeYf+V4BbI+J4RLwRuBc4M+cySFK15tq8k5mbEfFzwBfpd9n8rcx8bp5lmIOlbZqaIb8DvwPwO4AF/A4W+uYsSdJsVT2eviTVxtCXpIoY+jMQEcci4ssR8XxEPBcRH2m7TG2JiJWIeDYi/qDtsrQhIm6KiCci4q8j4oWI+OG2yzRvEfFg+T/4ekR8LiK+s+0yzUNE/FZEvBoRXx9Y99aIeCoiXizPN7dZRjD0Z2UT+Ghm3gbcAXw4Im5ruUxt+QjwQtuFaNGngT/OzO8HfojKvouIOAL8ArCWmT9Iv8PGve2Wam7+J3D30LqPAU9n5q3A0+V1qwz9GcjMlzPzq2X5dfr/6EfaLdX8RcRR4CeAR9suSxsi4hDwI8BpgMz8l8y80mqh2rEKfFdErAJvAv625fLMRWb+CXB5aPUJ4LGy/BhwzzzLNIqhP2MR0QXeCTzTclHa8DDwS8C/tVyOthwHLgG/XZq4Ho2IN7ddqHnKzIvAp4BvAi8DVzPzf7dbqlbdkpkvl+VXgFvaLAwY+jMVEd8N/D7wQGb+Q9vlmaeI+Eng1cw823ZZWrQKvAv4TGa+E/hHFuDn/DyVNusT9E+A3wu8OSL+c7ulWgzZ7x/feh95Q39GIuIN9AP/s5n5hbbL04I7gZ+KiB7wOPCeiPhf7RZp7i4AFzKz+ZX3BP2TQE1+FDifmZcy81+BLwD/oeUytelbEfF2gPL8asvlMfRnIfpj8J4GXsjMX2+7PG3IzI9n5tHM7NK/cPelzKyqhpeZrwAvRcQ7yqq72NdhwxfSN4E7IuJN5f/iLiq7mD3kDHCyLJ8EnmyxLIChPyt3Ah+kX7v9y/J4f9uFUit+HvhsRHwN+PfA/2i3OPNVfuU8AXwV+Cv6GbNwQxHsh4j4HPBnwDsi4kJE3Ad8EvixiHiR/q+gsTMFzovDMEhSRazpS1JFDH1JqoihL0kVMfQlqSKGviRVxNCXpIoY+pJUkf8PtZOu0p3Qnr4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "model = \"tradeoff_angle_no_bias\"\n",
    "color_dict = {0: \"red\", 1: \"green\", 2: \"blue\", 3: \"black\"}\n",
    "\n",
    "rt_means_by_choice = {i: [] for i in range(4)}\n",
    "\n",
    "params_tmp = ssms.config.model_config[model][\"default_params\"]\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"d\")] = 1.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vh\")] = 1.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vl2\")] = 1.0  # 1.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vl1\")] = 0.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"theta\")] = 0.0\n",
    "\n",
    "\n",
    "# params_tmp[ssms.config.model_config[model]['params'].index('theta')] = 0.0\n",
    "for k in range(10):\n",
    "    sim_out = simulator(model=model, theta=params_tmp, n_samples=50000)\n",
    "    for i in np.unique(sim_out[\"choices\"]):\n",
    "        plt.hist(\n",
    "            sim_out[\"rts\"][sim_out[\"choices\"] == i],\n",
    "            histtype=\"step\",\n",
    "            bins=30,\n",
    "            color=color_dict[i],\n",
    "            alpha=1,\n",
    "        )  # 0.1)\n",
    "        rt_means_by_choice[i].append(np.mean(sim_out[\"rts\"][sim_out[\"choices\"] == i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ssms.basic_simulators import simulator\n",
    "\n",
    "model = \"tradeoff_angle_no_bias\"\n",
    "color_dict = {0: \"red\", 1: \"green\", 2: \"blue\", 3: \"black\"}\n",
    "\n",
    "rt_means_by_choice_2 = {i: [] for i in range(4)}\n",
    "params_tmp = ssms.config.model_config[model][\"default_params\"]\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"d\")] = 0.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vh\")] = 1.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vl2\")] = 1.0  # 1.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"vl1\")] = 0.0\n",
    "params_tmp[ssms.config.model_config[model][\"params\"].index(\"theta\")] = 0.0\n",
    "\n",
    "for k in range(10):\n",
    "    sim_out = simulator(model=model, theta=params_tmp, n_samples=50000)\n",
    "    for i in np.unique(sim_out[\"choices\"]):\n",
    "        plt.hist(\n",
    "            sim_out[\"rts\"][sim_out[\"choices\"] == i],\n",
    "            histtype=\"step\",\n",
    "            bins=30,\n",
    "            color=color_dict[i],\n",
    "            alpha=1,\n",
    "        )\n",
    "        rt_means_by_choice_2[i].append(np.mean(sim_out[\"rts\"][sim_out[\"choices\"] == i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Sr3Z//uBO15vkR0fWnUryeJJ3dW1bcn771jxS96muz/8dWb+t43gz9e7WMTyt3m79to7h71NV580EHARe2c0/G/h/wEvG+lwCfHLCtvuArwEvBJ4G3PXktsDHgKu6+euBd+ySmqduC7wH+O+76Rx3bfcDByas/1/Au7v5dwPv3Q31jo2PfwCObOX53UDNFwL3AS/oln94p8bxJuvdrWN4Yr07MYZHp/PqCr2qHq6qO7v5bwOnGX7LtY+Jj0BIEuC1DB95AMNHIPzMbqh5k3/fuWzhMUcfL7Gwc7zAel8HfK2qVhdR13p61vyLwCeq6v93/R7p1m/7ON5Mvbt4DE87v+vZkjE86rwK9FEZPhHyFcCJCc2vTnJXkk8leWm3btIjEA4xfMTBt6rq3Nj63VDzrG2vTnJ3khu34te/Oest4K+SnMzwURFPel5VPdzN/wPwvF1S75OuAj4ytm5Lzy+sW/N/Bn4wyWe7c/mWbv2OjuM56p217U6N4fXq3bExvPBfB/fCBDwLOAn83IS2/wQ8q5u/DPhqN38l8IGRfm8GrmP4/IYzI+sPA/fshprX27YbTPsY/lD/PYbfL9jxeoFD3Z8/zPB2wH/tlr81to9v7oZ6u3VPY/gMj+dt1/ntUfN1wB3AM7sx+lWGIbRj43ieenfxGJ5a706N4arz7JYLQJL9wMeBD1fVJ8bbq+rxqnqim78V2J/hg6OmPQLhLHBhho88GF2/G2qeum1Vfb2qvltV3wP+D8NfxXe83qp6qPvzEeDPRur6epKD3f4PAn1+xd3yejtvAO6sqq+PbLNl57dPzQyvsD9dVf9YVd8APge8jB0ax5uod1eO4fXq3Ykx/KTzKtC7+4Q3AKer6n1T+jy/60eSixmeo7NMeQRCDX/U3s7wygeGj0D4i91Q83rbPjmwOj8L3LML6n1mkmd3658J/LeRukYfL7Gwc7zJMfGkNzF2u2Wrzm/fmhmen59KckGS/8jwCamn2YFxvJl6d+sYXqfebR/D32fRl/y7eQJ+iuH9rbuBU910GfCrwK92fa4G7mX4q9IdwH8Z2f4yhv/j/TXgt0fWvxD4InAG+FPg6buh5mnbdm0fAr7ctR0HDu6Cel/Yrburax89x88F/obhr7Z/DfzQTtfbtT2TYbg/Z2y/W3J++9bc9fsfDD+JcQ/wrp0ax5upd7eO4XXq3fYxPDr51X9JasR5dctFklpmoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG/BvvaZObvJftHQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    plt.hist(rt_means_by_choice[i], histtype=\"step\", color=\"red\")\n",
    "    plt.hist(rt_means_by_choice_2[i], histtype=\"step\", color=\"black\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "for i in np.unique(sim_out[\"choices\"]):\n",
    "    plt.hist(\n",
    "        sim_out[\"rts\"][sim_out[\"choices\"] == i],\n",
    "        histtype=\"step\",\n",
    "        bins=30,\n",
    "        color=color_dict[i],\n",
    "        alpha=0.5,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(np.array([0], dtype=np.float32), 10).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "c2404e761a8d4e2a34f63613cf4c9a9997cd3109cabb959a7904b2035989131a"
  },
  "kernelspec": {
   "display_name": "hddmnn_tutorial",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
